Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method, the method being performed in computerized system comprising a central processing unit, a localization signal receiver and a memory, the computer-implemented method comprising, iteratively: receiving at least one localization signal using the localization signal receiver; determining a round trip time for the at least one localization signal; determining at least one velocity parameter of a user's motion state based on the determined round trip time and a preceding round trip time of a preceding iteration; executing a particle filter loop based at least on the received localization signal and the determined at least one velocity parameter of the user's motion, the particle filter loop comprises performing a measurement update using at least the determined round trip time and calculating a distance between each of a plurality of particles and at least one radiofrequency beacon based on the received radiofrequency localization signal, performing a motion update using at least the at least one velocity parameter, and resampling a plurality of particles; and determining a position of the user based at least on the execution of the particle filter loop, wherein determining the position of the user comprises continuously executing the particle filter loop in parallel with determining the at least one velocity parameter.
This invention relates to a computer-implemented method for real-time user localization using radiofrequency signals. The system addresses the challenge of accurately tracking a user's position in dynamic environments where signal conditions may vary, by combining round-trip time measurements with a particle filter algorithm to refine position estimates. The method operates within a computerized system that includes a central processing unit, a localization signal receiver, and memory. It iteratively receives radiofrequency localization signals from at least one beacon, calculates the round-trip time for each signal, and derives velocity parameters by comparing current and preceding round-trip times. These velocity parameters are used to model the user's motion state. A particle filter loop processes the localization signals and velocity data to estimate the user's position. The loop includes a measurement update that calculates distances between particles (hypothetical position estimates) and the beacon based on the round-trip time, a motion update that adjusts particles using the velocity parameters, and a resampling step to refine the particle distribution. The particle filter runs continuously in parallel with velocity parameter determination, ensuring real-time position updates. This approach improves localization accuracy by dynamically incorporating motion dynamics and signal measurements.
2. The computer-implemented method of claim 1 , wherein performing the measurement update comprises updating a plurality of particles using a confidence of a classifier.
This invention relates to a computer-implemented method for improving particle filtering in tracking systems, particularly where uncertainty in measurements or classifications affects tracking accuracy. Particle filtering, a probabilistic technique used in tracking objects, relies on a set of weighted particles to estimate the state of a system. However, when measurements or classifications are uncertain, the accuracy of these estimates can degrade. The invention addresses this by incorporating a classifier's confidence into the measurement update step of particle filtering. During the measurement update, a plurality of particles are adjusted based on the confidence level of a classifier, which provides a probabilistic assessment of the measurement's reliability. This ensures that particles are weighted more accurately, reducing errors caused by low-confidence measurements. The method enhances tracking performance in scenarios where measurements are noisy or uncertain, such as in computer vision, robotics, or sensor-based systems. By dynamically adjusting particle weights according to classifier confidence, the system achieves more robust and reliable state estimation.
3. The computer-implemented method of claim 1 , wherein resampling the plurality of particles comprises calculating a weight for each of a plurality of particles and replicating particles of the plurality of particles with higher weights.
This invention relates to particle filtering techniques used in computational systems, particularly for improving the accuracy and efficiency of state estimation in dynamic systems. The problem addressed is the degradation of particle filtering performance due to particle depletion, where certain particles become dominant, leading to poor diversity and inaccurate estimates. The method involves resampling a set of particles to mitigate this issue. Each particle is assigned a weight based on its likelihood of representing the true system state. Particles with higher weights are replicated, while those with lower weights are discarded or reduced in number. This ensures that the particle set remains diverse and representative of the system's possible states, improving estimation accuracy. The resampling process is applied iteratively, allowing the system to adapt to changing conditions. By dynamically adjusting the particle distribution, the method maintains a balanced representation of possible states, reducing the risk of convergence to suboptimal solutions. This approach is particularly useful in applications such as tracking, navigation, and sensor fusion, where accurate state estimation is critical. The technique enhances computational efficiency by focusing resources on the most relevant particles, avoiding unnecessary computations on less likely states.
4. The computer-implemented method of claim 1 , wherein the measurement update is performed based on a second received at least one localization signal.
A computer-implemented method for improving localization accuracy in a system that relies on received signals involves updating position estimates using additional signal measurements. The method addresses the challenge of inaccurate or unreliable position estimates in environments where signal quality or availability is inconsistent. The system initially determines an initial position estimate based on a first set of localization signals, such as GPS, Wi-Fi, or other wireless signals. To refine this estimate, the method performs a measurement update using a second set of received localization signals. This update process adjusts the position estimate by incorporating the new signal data, reducing errors caused by signal noise, interference, or environmental factors. The method may involve filtering techniques, such as Kalman filtering, to combine the initial estimate with the updated measurements, ensuring a more accurate and reliable final position. The approach is particularly useful in applications requiring precise localization, such as autonomous navigation, asset tracking, or indoor positioning systems. By leveraging multiple signal measurements, the method enhances the robustness and accuracy of position determination in dynamic or challenging environments.
5. The computer-implemented method of claim 1 , wherein the motion update is performed based on a second velocity parameter of the user's motion state.
This invention relates to computer-implemented methods for updating motion states in a virtual environment, particularly for applications like virtual reality (VR) or augmented reality (AR). The problem addressed is accurately tracking and updating a user's motion state in real-time to enhance immersion and responsiveness in virtual environments. The method involves determining a motion update for a user's motion state, where the update is based on a second velocity parameter derived from the user's motion. This velocity parameter helps refine the motion tracking by incorporating dynamic changes in speed or direction, improving the accuracy of the virtual representation of the user's movements. The motion update may also involve adjusting the user's position or orientation in the virtual environment based on the calculated velocity, ensuring smooth and realistic interactions. Additionally, the method may include filtering or smoothing the motion data to reduce noise and enhance stability. The invention aims to provide a more precise and responsive motion tracking system, which is critical for applications requiring high-fidelity user interaction, such as gaming, training simulations, or industrial design.
6. The computer-implemented method of claim 1 , wherein receiving the at least one localization signal comprises determining a strength of the at least one localization signal.
This invention relates to a computer-implemented method for processing localization signals in a wireless communication system. The method addresses the challenge of accurately determining the position of a device by analyzing the strength of received localization signals. The system receives at least one localization signal, which may be transmitted by a network of reference points or beacons. The method includes determining the signal strength of the received localization signal, which is then used to estimate the device's position. Signal strength measurements are often used in techniques like received signal strength indication (RSSI) or fingerprinting to infer distance or location. The method may involve comparing the measured signal strength against a reference database or applying signal propagation models to refine the position estimate. The system may also incorporate additional signals or environmental factors to improve accuracy. This approach is particularly useful in indoor positioning, asset tracking, or navigation systems where GPS signals are unreliable. The method enhances localization accuracy by leveraging signal strength analysis, which can compensate for multipath effects or signal interference. The system may further include calibration steps to account for variations in signal propagation due to obstacles or environmental changes. The overall goal is to provide a robust and precise localization solution for wireless devices in dynamic environments.
7. The computer-implemented method of claim 1 , wherein the at least one localization signal is a radiofrequency (RF) signal.
This invention relates to a computer-implemented method for enhancing localization accuracy in wireless communication systems. The method addresses the challenge of precisely determining the position of a device in environments where signal interference, multipath effects, or other factors degrade localization performance. The core technique involves processing at least one localization signal, which in this embodiment is a radiofrequency (RF) signal, to improve positional accuracy. The method may include receiving the RF signal from one or more transmitters, analyzing signal characteristics such as time of arrival, signal strength, or phase differences, and applying computational algorithms to derive a more accurate position estimate. The system may also incorporate additional signals, such as ultrasonic or optical signals, to further refine localization. The method is particularly useful in applications requiring high-precision positioning, such as indoor navigation, asset tracking, or autonomous vehicle operations. By leveraging RF signals, the system can achieve reliable localization even in challenging environments where other signal types may fail. The technique may be implemented in software, hardware, or a combination of both, and can be integrated into existing wireless infrastructure or standalone localization devices.
8. The computer-implemented method of claim 1 , wherein the at least one localization signal is a Bluetooth Low Energy (BLE) signal.
This invention relates to a computer-implemented method for determining the location of a device using wireless signals, specifically Bluetooth Low Energy (BLE) signals. The method addresses the challenge of accurately locating devices in environments where traditional positioning systems like GPS may be unreliable or unavailable, such as indoors or in dense urban areas. The method involves receiving at least one BLE signal from a transmitting device, where the signal contains information that can be used to estimate the device's position. The system processes this signal to determine the device's location, leveraging characteristics such as signal strength, timing, or direction to improve accuracy. The method may also incorporate additional techniques, such as triangulation or fingerprinting, to refine the location estimate. By using BLE signals, the method provides a power-efficient and widely compatible solution for indoor positioning, enabling applications like asset tracking, navigation, and proximity-based services. The approach is particularly useful in scenarios where precise location data is needed without requiring specialized hardware beyond standard BLE-enabled devices. The system can be integrated into existing wireless networks, making it scalable and cost-effective for deployment in various environments.
9. The computer-implemented method of claim 1 , wherein the at least one localization signal is a WIFI round trip time (WIFI RTT) signal.
This invention relates to wireless localization techniques, specifically improving the accuracy of determining a device's position using Wi-Fi Round Trip Time (RTT) signals. The problem addressed is the inherent inaccuracy in traditional Wi-Fi positioning methods, which often rely on received signal strength indicators (RSSI) or time-of-flight (ToF) measurements that are susceptible to environmental interference and multipath effects. Wi-Fi RTT provides a more precise measurement by calculating the time it takes for a signal to travel from a device to an access point and back, enabling more accurate distance estimation. The method involves a device transmitting a signal to a Wi-Fi access point, which then reflects the signal back to the device. The time taken for this round trip is measured, and the distance between the device and the access point is calculated based on the speed of the signal. By using multiple access points, the device's position can be triangulated with higher precision than traditional methods. The technique leverages existing Wi-Fi infrastructure, making it cost-effective and widely deployable without requiring additional hardware. The invention also includes error correction mechanisms to account for signal propagation delays, multipath interference, and environmental factors that could distort the RTT measurements. These corrections improve the reliability of the distance calculations, leading to more accurate positioning. The method is particularly useful in indoor environments where GPS signals are weak or unavailable, such as in buildings, shopping malls, and industrial facilities. By enhancing the precision of Wi-Fi-based localization, the invention enables applications like asset tracking, indoor navigation, and location-based services.
10. The computer-implemented method of claim 1 , wherein determining a position of the user comprises determining coordinates of the user.
A system and method for tracking user position within a defined space, such as a building or outdoor area, to enable location-based services or navigation assistance. The technology addresses the challenge of accurately determining a user's position in environments where traditional GPS signals may be weak or unavailable, such as indoors or in urban canyons. The method involves using sensor data, such as from a mobile device or wearable device, to calculate the user's coordinates within a predefined coordinate system. This may include processing signals from Wi-Fi, Bluetooth, or other wireless beacons, as well as inertial measurement unit (IMU) data, to estimate the user's location. The coordinates are then used to provide real-time positioning information, which can be integrated with mapping or navigation applications to guide the user or deliver location-specific content. The system may also incorporate calibration techniques to improve accuracy, such as correcting for signal interference or device orientation. The method ensures reliable positioning even in challenging environments, enabling applications like indoor navigation, asset tracking, and location-based advertising.
11. A non-transitory computer-readable medium embodying a set of computer-executable instructions, which, when executed in connection with a system comprising a central processing unit, a localization signal receiver and a memory, cause the system to perform a method comprising, iteratively: receiving at least one localization signal using the localization signal receiver; determining a round trip time for the at least one localization signal; determining at least one velocity parameter of a user's motion state based on the determined round trip time and a preceding round trip time of a preceding iteration; executing a particle filter loop based at least on the received localization signal and the determined at least one velocity parameter of the user's motion, the particle filter loop comprises performing a measurement update using at least the determined round trip time and calculating a distance between each of a plurality of particles and at least one radiofrequency beacon based on the received radiofrequency localization signal, performing a motion update using at least the at least one velocity parameter, and resampling a plurality of particles; and determining a position of the user based at least on the execution of the particle filter loop, wherein determining the position of the user comprises continuously executing the particle filter loop in parallel with determining the at least one velocity parameter.
This invention relates to indoor positioning systems that use radiofrequency (RF) signals to track a user's location. The problem addressed is the challenge of accurately determining a user's position in real-time, especially in environments where GPS signals are unreliable or unavailable. The solution involves a computational method that processes RF localization signals to estimate the user's position and motion state dynamically. The system includes a central processing unit, an RF signal receiver, and memory. The method iteratively receives RF localization signals and calculates the round trip time (RTT) for each signal. By comparing the current RTT with a previous RTT from a prior iteration, the system determines velocity parameters that describe the user's motion. These parameters are used in a particle filter loop, which is a probabilistic technique for estimating position. The particle filter loop involves three steps: a measurement update, where the RTT is used to calculate distances between multiple particles (hypothetical position estimates) and RF beacons; a motion update, which adjusts the particles based on the velocity parameters; and resampling, which refines the particles to improve accuracy. The position is determined by continuously executing this loop in parallel with the velocity parameter calculations, ensuring real-time tracking. This approach improves accuracy by dynamically incorporating motion data into the positioning algorithm.
12. A system comprising a central processing unit, a localization signal receiver and a memory, the memory storing a set of computer-readable instructions causing the system to perform a method comprising, iteratively: receiving at least one localization signal using the localization signal receiver; determining a round trip time for the at least one localization signal; determining at least one velocity parameter of a user's motion state based on the determined round trip time and a preceding round trip time of a preceding iteration; executing a particle filter loop based at least on the received localization signal and the determined at least one velocity parameter of the user's motion, the particle filter loop comprises performing a measurement update using at least the determined round trip time and calculating a distance between each of a plurality of particles and at least one radiofrequency beacon based on the received radiofrequency localization signal, performing a motion update using at least the at least one velocity parameter, and resampling a plurality of particles; and determining a position of the user based at least on the execution of the particle filter loop, wherein determining the position of the user comprises continuously executing the particle filter loop in parallel with determining the at least one velocity parameter.
This invention relates to a system for real-time indoor positioning using radiofrequency (RF) signals. The system addresses the challenge of accurately tracking a user's position in environments where GPS signals are unreliable, such as indoors, by leveraging RF localization signals and motion dynamics. The system includes a central processing unit, a localization signal receiver, and a memory storing instructions for iterative processing. The receiver captures RF signals from beacons, and the system calculates the round trip time (RTT) for each signal. By comparing the RTT with a previous iteration's RTT, the system estimates the user's velocity parameters, such as speed and direction. A particle filter loop is executed in parallel with velocity estimation to refine position tracking. The filter incorporates the RTT data to update particle weights, representing possible user positions. Particles are resampled based on their weights, and their distances to nearby RF beacons are recalculated. The motion update step adjusts particle positions using the estimated velocity parameters. The system continuously updates the user's position by iterating this process, ensuring real-time accuracy. This approach combines RF signal analysis with motion dynamics to improve localization precision in dynamic environments, making it suitable for applications like asset tracking, navigation, and augmented reality.
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December 22, 2020
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